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Mining Time Variant Frequent Pattern using PPM and PWM: A Comparison
by
P. A. Shirsath,
Vijay Kumar Verma
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 77 - Number 15 |
| Year of Publication: 2013 |
| Authors: P. A. Shirsath, Vijay Kumar Verma |
10.5120/13558-1314
|
P. A. Shirsath, Vijay Kumar Verma . Mining Time Variant Frequent Pattern using PPM and PWM: A Comparison. International Journal of Computer Applications. 77, 15 ( September 2013), 12-17. DOI=10.5120/13558-1314
@article{
10.5120/13558-1314,
author = {
P. A. Shirsath,
Vijay Kumar Verma
},
title = { Mining Time Variant Frequent Pattern using PPM and PWM: A Comparison },
journal = {
International Journal of Computer Applications
},
issue_date = { September 2013 },
volume = { 77 },
number = { 15 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = {
12-17
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume77/number15/13558-1314/
},
doi = { 10.5120/13558-1314 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:48:53.807307+05:30
%A P. A. Shirsath
%A Vijay Kumar Verma
%T Mining Time Variant Frequent Pattern using PPM and PWM: A Comparison
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 15
%P 12-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The process of exploring and analyzing data from different perspective, using automatic or semiautomatic techniques is called Data mining. Data mining extracts knowledge or useful information and discovers correlations or meaningful patterns and rules from large databases [1, 2]. Using these patterns and rules it is possible for business enterprises to identify new and unexpected trends, subtle relations in the data and use them to increase revenue and cut cost. In this paper we proposed a comparative study over Progressive Partition Miner (PPM) and Progressive Weighted miner (PWM).
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Index Terms
Computer Science
Information Sciences
Keywords